Faculty Bibliography

Sequencing DNA in a synthetic solid-state nanopore is potentially a low-cost and high-throughput method. Essential to the nanopore-based DNA sequencing method is the ability to control the motion of a single-stranded DNA (ssDNA) molecule at single-base resolution. Experimental studies showed that the average translocation speed of DNA driven by a biasing electric field can be affected by ionic concentration, solvent viscosity, or temperature. Even though it is possible to slow down the average translocation speed, instantaneous motion of DNA is too diffusive to allow each DNA base to stay in front of a sensor site for its measurement. Using extensive all-atom molecular dynamics simulations, we study the diffusion constant, friction coefficient, electrophoretic mobility, and effective charge of ssDNA in a solid-state nanopore. Simulation results show that the spatial fluctuation of ssDNA in 1 ns is comparable to the spacing between neighboring nucleotides in ssDNA, which makes the sensing of a DNA base very difficult. We demonstrate that the recently proposed DNA transistor could potentially solve this problem by electrically trapping ssDNA inside the DNA transistor and ratcheting ssDNA base-by-base in a biasing electric field. When increasing the biasing electric field, we observed that the translocation of ssDNA changes from ratcheting to steady-sliding. The simulated translocation of ssDNA in the DNA transistor was theoretically characterized using Fokker-Planck analysis.

BACKGROUND:Systems biology has embraced computational modeling in response to the quantitative nature and increasing scale of contemporary data sets. The onslaught of data is accelerating as molecular profiling technology evolves. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) is a community effort to catalyze discussion about the design, application, and assessment of systems biology models through annual reverse-engineering challenges. METHODOLOGY AND PRINCIPAL FINDINGS/RESULTS:We describe our assessments of the four challenges associated with the third DREAM conference which came to be known as the DREAM3 challenges: signaling cascade identification, signaling response prediction, gene expression prediction, and the DREAM3 in silico network challenge. The challenges, based on anonymized data sets, tested participants in network inference and prediction of measurements. Forty teams submitted 413 predicted networks and measurement test sets. Overall, a handful of best-performer teams were identified, while a majority of teams made predictions that were equivalent to random. Counterintuitively, combining the predictions of multiple teams (including the weaker teams) can in some cases improve predictive power beyond that of any single method. CONCLUSIONS:DREAM provides valuable feedback to practitioners of systems biology modeling. Lessons learned from the predictions of the community provide much-needed context for interpreting claims of efficacy of algorithms described in the scientific literature.

Numerous methods have been developed for inferring gene regulatory networks from expression data, however, both their absolute and comparative performance remain poorly understood. In this paper, we introduce a framework for critical performance assessment of methods for gene network inference. We present an in silico benchmark suite that we provided as a blinded, community-wide challenge within the context of the DREAM (Dialogue on Reverse Engineering Assessment and Methods) project. We assess the performance of 29 gene-network-inference methods, which have been applied independently by participating teams. Performance profiling reveals that current inference methods are affected, to various degrees, by different types of systematic prediction errors. In particular, all but the best-performing method failed to accurately infer multiple regulatory inputs (combinatorial regulation) of genes. The results of this community-wide experiment show that reliable network inference from gene expression data remains an unsolved problem, and they indicate potential ways of network reconstruction improvements.

We investigate the base-by-base translocation dynamics of single-stranded DNA (ssDNA) confined in a solid-state nanopore dressed with an electrostatic trap, using all-atom molecular dynamics (MD) simulation. We observe on the simulation time scale of tens of nanoseconds that ssDNA can be driven through the nanopore in a ratchetlike fashion, with a step size equal to the spacing between neighboring phosphate groups in the ssDNA backbone. A 1D-Langevin-like model is derived from atomistic dynamics which can quantitatively describe simulation results and can be used to study dynamics on longer time scales. The controlled ratcheting motion of DNA could potentially enhance the signal-to-noise ratio for nanoelectronic DNA sensing technologies.

BACKGROUND:The vast computational resources that became available during the past decade enabled the development and simulation of increasingly complex mathematical models of cancer growth. These models typically involve many free parameters whose determination is a substantial obstacle to model development. Direct measurement of biochemical parameters in vivo is often difficult and sometimes impracticable, while fitting them under data-poor conditions may result in biologically implausible values. RESULTS:We discuss different methodological approaches to estimate parameters in complex biological models. We make use of the high computational power of the Blue Gene technology to perform an extensive study of the parameter space in a model of avascular tumor growth. We explicitly show that the landscape of the cost function used to optimize the model to the data has a very rugged surface in parameter space. This cost function has many local minima with unrealistic solutions, including the global minimum corresponding to the best fit. CONCLUSIONS:The case studied in this paper shows one example in which model parameters that optimally fit the data are not necessarily the best ones from a biological point of view. To avoid force-fitting a model to a dataset, we propose that the best model parameters should be found by choosing, among suboptimal parameters, those that match criteria other than the ones used to fit the model. We also conclude that the model, data and optimization approach form a new complex system and point to the need of a theory that addresses this problem more generally.

The DNA-Transistor is a device designed to control the translocation of single-stranded DNA through a solid-state nanopore. Functionality of the device is enabled by three electrodes exposed to the DNA-containing electrolyte solution within the pore and the application of a dynamic electrostatic potential well between the electrodes to temporarily trap a DNA molecule. Optimizing the surface chemistry and electrochemical behavior of the device is a necessary (but by no means sufficient) step toward the development of a functional device. In particular, effects to be eliminated are (i) electrochemically induced surface alteration through corrosion or reduction of the electrode surface and (ii) formation of hydrogen or oxygen bubbles inside the pore through water decomposition. Even though our motivation is to solve problems encountered in DNA transistor technology, in this paper we report on generic surface chemistry results. We investigated a variety of electrode-electrolyte-solvent systems with respect to their capability of suppressing water decomposition and maintaining surface integrity. We employed cyclic voltammetry and long-term amperometry as electrochemical test schemes, X-ray photoelectron spectroscopy, atomic force microscopy, and scanning, as well as transmission electron microscopy as analytical tools. Characterized electrode materials include thin films of Ru, Pt, nonstoichiometric TiN, and nonstoichiometric TiN carrying a custom-developed titanium oxide layer, as well as custom-oxidized nonstoichiometric TiN coated with a monolayer of hexadecylphosphonic acid (HDPA). We used distilled water as well as aqueous solutions of poly(ethylene glycol) (PEG-300) and glycerol as solvents. One millimolar KCl was employed as electrolyte in all solutions. Our results show that the HDPA-coated custom-developed titanium oxide layer effectively passivates the underlying TiN layer, eliminating any surface alterations through corrosion or reduction within a voltage window from -2 V to +2 V. Furthermore, we demonstrated that, by coating the custom-oxidized TiN samples with HDPA and increasing the concentration of PEG-300 or glycerol in aqueous 1 mM KCl solutions, water decomposition was suppressed within the same voltage window. Water dissociation was not detected when combining custom-oxidized HDPA-coated TiN electrodes with an aqueous 1 mM KCl-glycerol solution at a glycerol concentration of at least 90%. These results are applicable to any system that requires nanoelectrodes placed in aqueous solution at voltages that can activate electrochemical processes.

A biomimetic nanochannel coated with a self-assembled monolayer (SAM) can be used for sensing and analyzing biomolecules. The interaction between a transported biomolecule and a SAM governs the mechanically or electrically driven motion of the molecule. To investigate the translocation dynamics of a biomolecule, we performed all-atom molecular dynamics simulations on a single-stranded DNA in a solid-state nanochannel coated with a SAM that consists of octane or octanol polymers. Simulation results demonstrate that the interaction between DNA and a hydrophobic or a hydrophilic SAM is effectively repulsive or adhesive, respectively, resulting in different translocation dynamics of DNA. Therefore, with proper designs of SAMs coated on a channel surface, it is possible to control the translocation dynamics of a biomolecule. This work also demonstrates that traditional tribology methods can be deployed to study a biological or biomimetic transport process.

ChIP-on-chip has emerged as a powerful tool to dissect the complex network of regulatory interactions between transcription factors and their targets. However, most ChIP-on-chip analysis methods use conservative approaches aimed at minimizing false-positive transcription factor targets. We present a model with improved sensitivity in detecting binding events from ChIP-on-chip data. Its application to human T cells, followed by extensive biochemical validation, reveals that 3 oncogenic transcription factors, NOTCH1, MYC, and HES1, bind to several thousand target gene promoters, up to an order of magnitude increase over conventional analysis methods. Gene expression profiling upon NOTCH1 inhibition shows broad-scale functional regulation across the entire range of predicted target genes, establishing a closer link between occupancy and regulation. Finally, the increased sensitivity reveals a combinatorial regulatory program in which MYC cobinds to virtually all NOTCH1-bound promoters. Overall, these results suggest an unappreciated complexity of transcriptional regulatory networks and highlight the fundamental importance of genome-scale analysis to represent transcriptional programs.

Regardless of how creative, innovative, and elegant our computational methods, the ultimate proof of an algorithm's worth is the experimentally validated quality of its predictions. Unfortunately, this truism is hard to reduce to practice. Usually, modelers produce hundreds to hundreds of thousands of predictions, most (if not all) of which go untested. In a best-case scenario, a small subsample of predictions (three to ten usually) is experimentally validated, as a quality control step to attest to the global soundness of the full set of predictions. However, whether this small set is even representative of the global algorithm's performance is a question usually left unaddressed. Thus, a clear understanding of the strengths and weaknesses of an algorithm most often remains elusive, especially to the experimental biologists who must decide which tool to use to address a specific problem. In this chapter, we describe the first systematic set of challenges posed to the systems biology community in the framework of the DREAM (Dialogue for Reverse Engineering Assessments and Methods) project. These tests, which came to be known as the DREAM2 challenges, consist of data generously donated by participants to the DREAM project and curated in such a way as to become problems of network reconstruction and whose solutions, the actual networks behind the data, were withheld from the participants. The explanation of the resulting five challenges, a global comparison of the submissions, and a discussion of the best performing strategies are the main topics discussed.